
The provided text introduces WritingBench, a new and comprehensive benchmark for evaluating the generative writing capabilities of large language models (LLMs) across a wide range of domains and writing tasks. To address limitations in existing benchmarks, WritingBench features a diverse set of queries and proposes a query-dependent evaluation framework. This framework dynamically generates instance-specific assessment criteria using LLMs and employs a fine-tuned critic model for scoring responses based on these criteria, considering aspects like style, format, and length. The benchmark and its associated tools are open-sourced to promote advancements in LLM writing abilities, and experiments demonstrate the effectiveness of its evaluation framework in data curation and model training.
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